1887

Abstract

A better understanding of human immunodeficiency virus type 1 drug-resistance evolution under the selective pressure of combination treatment is important for the design of long-term effective treatment strategies. We applied Bayesian network learning to sequences from patients treated with the reverse transcriptase inhibitor combination of zidovudine (AZT) and lamivudine (3TC) to identify the role of many treatment-selected mutations in the development of resistance. Based on the Bayesian network structure, an fitness landscape was built, reflecting the necessary selective pressure under treatment, to evolve naive sequences to sequences obtained from patients treated with the combination. This landscape, combined with an evolutionary model, was used to predict resistance evolution in longitudinal sequence pairs. In our analysis, mutations 41L, 70R, 184V and 215F/Y were identified as major resistance mutations to the combination of AZT and 3TC, as they were associated directly with treatment experience. The network also suggested a possible role in resistance development for a number of novel mutations. Estimated fitness, using the landscape, correlated significantly with resistance phenotype in genotype–phenotype pairs ( =0.70). Variation in predicted evolution under selective pressure correlated significantly with observed evolution during AZT plus 3CT treatment. In conclusion, we confirmed current knowledge on resistance development to the combination of AZT and 3CT, but additional novel mutations were identified. Moreover, a model to predict resistance evolution during AZT and 3CT treatment has been built and validated.

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2010-08-01
2019-11-22
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Modelling HIV drug resistance evolution [ PDF] (558 KB)

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Confounding effect of epidemiology [ PDF] (101 KB)

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Annotated AZT+3TC experience BN showing direct influences between therapy-associated mutations, polymorphisms and AZT+3TC experience [ PDF] (149 KB)

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Dataset prevalence (%) of mutations included in the BN [ PDF] (194 KB)

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Reverse transcriptase mutations included in the fitness function [ PDF] (141 KB)

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Correlation of variation in predicted with observed evolution in longitudinal sequence pairs [ PDF] (152 KB)

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